from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-23 14:07:09.319668
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Mon, 23, Nov, 2020
Time: 14:07:12
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -42.5014
Nobs: 119.000 HQIC: -43.7498
Log likelihood: 1224.21 FPE: 4.27167e-20
AIC: -44.6033 Det(Omega_mle): 2.06639e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.719214 0.212310 3.388 0.001
L1.Burgenland 0.130204 0.091755 1.419 0.156
L1.Kärnten -0.311282 0.077190 -4.033 0.000
L1.Niederösterreich -0.005402 0.223903 -0.024 0.981
L1.Oberösterreich 0.273025 0.180935 1.509 0.131
L1.Salzburg 0.123637 0.090748 1.362 0.173
L1.Steiermark 0.087426 0.129006 0.678 0.498
L1.Tirol 0.165777 0.085150 1.947 0.052
L1.Vorarlberg 0.009282 0.084485 0.110 0.913
L1.Wien -0.156522 0.174311 -0.898 0.369
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.727925 0.271598 2.680 0.007
L1.Burgenland -0.012755 0.117378 -0.109 0.913
L1.Kärnten 0.345723 0.098746 3.501 0.000
L1.Niederösterreich 0.064430 0.286428 0.225 0.822
L1.Oberösterreich -0.212067 0.231462 -0.916 0.360
L1.Salzburg 0.156635 0.116089 1.349 0.177
L1.Steiermark 0.193574 0.165031 1.173 0.241
L1.Tirol 0.135299 0.108929 1.242 0.214
L1.Vorarlberg 0.194112 0.108078 1.796 0.072
L1.Wien -0.561534 0.222987 -2.518 0.012
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.352006 0.091087 3.865 0.000
L1.Burgenland 0.105951 0.039366 2.691 0.007
L1.Kärnten -0.028467 0.033117 -0.860 0.390
L1.Niederösterreich 0.127321 0.096060 1.325 0.185
L1.Oberösterreich 0.267517 0.077626 3.446 0.001
L1.Salzburg -0.010267 0.038933 -0.264 0.792
L1.Steiermark -0.061811 0.055347 -1.117 0.264
L1.Tirol 0.097099 0.036532 2.658 0.008
L1.Vorarlberg 0.145458 0.036247 4.013 0.000
L1.Wien 0.015233 0.074784 0.204 0.839
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.209356 0.107952 1.939 0.052
L1.Burgenland 0.006099 0.046654 0.131 0.896
L1.Kärnten 0.034228 0.039248 0.872 0.383
L1.Niederösterreich 0.091469 0.113847 0.803 0.422
L1.Oberösterreich 0.350138 0.091999 3.806 0.000
L1.Salzburg 0.086873 0.046142 1.883 0.060
L1.Steiermark 0.195213 0.065595 2.976 0.003
L1.Tirol 0.027019 0.043296 0.624 0.533
L1.Vorarlberg 0.112200 0.042958 2.612 0.009
L1.Wien -0.112679 0.088631 -1.271 0.204
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.876359 0.234261 3.741 0.000
L1.Burgenland 0.051667 0.101242 0.510 0.610
L1.Kärnten -0.019504 0.085171 -0.229 0.819
L1.Niederösterreich -0.132852 0.247053 -0.538 0.591
L1.Oberösterreich 0.051014 0.199643 0.256 0.798
L1.Salzburg 0.040475 0.100130 0.404 0.686
L1.Steiermark 0.115010 0.142344 0.808 0.419
L1.Tirol 0.234039 0.093954 2.491 0.013
L1.Vorarlberg 0.029025 0.093221 0.311 0.756
L1.Wien -0.211506 0.192333 -1.100 0.271
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.188206 0.160395 1.173 0.241
L1.Burgenland -0.040319 0.069319 -0.582 0.561
L1.Kärnten -0.010365 0.058315 -0.178 0.859
L1.Niederösterreich 0.205901 0.169153 1.217 0.224
L1.Oberösterreich 0.395749 0.136692 2.895 0.004
L1.Salzburg -0.038221 0.068558 -0.558 0.577
L1.Steiermark -0.055497 0.097461 -0.569 0.569
L1.Tirol 0.195962 0.064329 3.046 0.002
L1.Vorarlberg 0.055174 0.063827 0.864 0.387
L1.Wien 0.114064 0.131687 0.866 0.386
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.336342 0.204446 1.645 0.100
L1.Burgenland 0.066978 0.088357 0.758 0.448
L1.Kärnten -0.082402 0.074331 -1.109 0.268
L1.Niederösterreich -0.146517 0.215610 -0.680 0.497
L1.Oberösterreich -0.119488 0.174234 -0.686 0.493
L1.Salzburg -0.008578 0.087387 -0.098 0.922
L1.Steiermark 0.383316 0.124228 3.086 0.002
L1.Tirol 0.537055 0.081997 6.550 0.000
L1.Vorarlberg 0.225170 0.081356 2.768 0.006
L1.Wien -0.180183 0.167855 -1.073 0.283
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.201130 0.234520 0.858 0.391
L1.Burgenland 0.012778 0.101354 0.126 0.900
L1.Kärnten -0.071205 0.085265 -0.835 0.404
L1.Niederösterreich 0.208058 0.247325 0.841 0.400
L1.Oberösterreich 0.013967 0.199863 0.070 0.944
L1.Salzburg 0.226504 0.100241 2.260 0.024
L1.Steiermark 0.160765 0.142501 1.128 0.259
L1.Tirol 0.053389 0.094058 0.568 0.570
L1.Vorarlberg 0.001640 0.093323 0.018 0.986
L1.Wien 0.209575 0.192545 1.088 0.276
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.683497 0.129561 5.275 0.000
L1.Burgenland -0.010949 0.055993 -0.196 0.845
L1.Kärnten -0.012035 0.047105 -0.256 0.798
L1.Niederösterreich -0.080752 0.136635 -0.591 0.555
L1.Oberösterreich 0.269077 0.110414 2.437 0.015
L1.Salzburg 0.000423 0.055378 0.008 0.994
L1.Steiermark 0.009509 0.078725 0.121 0.904
L1.Tirol 0.079080 0.051962 1.522 0.128
L1.Vorarlberg 0.183005 0.051557 3.550 0.000
L1.Wien -0.107704 0.106372 -1.013 0.311
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.079376 -0.064666 0.194311 0.233218 0.015099 0.065657 -0.141719 0.093358
Kärnten 0.079376 1.000000 -0.066805 0.175540 0.067918 -0.162776 0.173071 0.007921 0.274823
Niederösterreich -0.064666 -0.066805 1.000000 0.229846 0.053753 0.145092 0.070566 0.041195 0.353264
Oberösterreich 0.194311 0.175540 0.229846 1.000000 0.241565 0.261965 0.070440 0.055076 0.030459
Salzburg 0.233218 0.067918 0.053753 0.241565 1.000000 0.136396 0.043515 0.066139 -0.076298
Steiermark 0.015099 -0.162776 0.145092 0.261965 0.136396 1.000000 0.096266 0.095451 -0.205365
Tirol 0.065657 0.173071 0.070566 0.070440 0.043515 0.096266 1.000000 0.130892 0.082030
Vorarlberg -0.141719 0.007921 0.041195 0.055076 0.066139 0.095451 0.130892 1.000000 0.065346
Wien 0.093358 0.274823 0.353264 0.030459 -0.076298 -0.205365 0.082030 0.065346 1.000000